IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i14p7586-d589870.html
   My bibliography  Save this article

Decomposition Analysis of Global Water Supply-Demand Balances Focusing on Food Production and Consumption

Author

Listed:
  • Yohei Yamaguchi

    (Graduate School of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan)

  • Naoki Yoshikawa

    (College of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan)

  • Koji Amano

    (College of Gastronomy Management, Ritsumeikan University, Kusatsu 525-8577, Japan)

  • Seiji Hashimoto

    (College of Science and Engineering, Ritsumeikan University, Kusatsu 525-8577, Japan)

Abstract

Food production and consumption require large amounts of freshwater. There is no literature on the decomposition analysis of the intensities of water supply-demand balances (water balance intensities) for each country worldwide. The aim of this study is to evaluate the water balance intensities and elucidate the promoting factors and offset factors of water balance intensities for each country worldwide, focusing on food supply-demand balances and considering food trade balances on a global scale. The modified Laspeyres index method is applied to both a production-based water balance index ( WBI PB ) and a consumption-based water balance index ( WBI CB ). The major promoting factor for the WBI PB is the renewable freshwater resources, whereas the major offset factor is the produced item preference. The major promoting factor for the WBI CB is the consumed item preference, whereas the major offset factor is the producing area preference. Improving irrigation efficiencies of rice and cereals is effective because rice requires the largest blue water footprint intensities, considering irrigation efficiency on a calorie content basis in all of the items, whereas cereals are the largest share of calorie-based production quantities in all of the items worldwide. This study provides the foundation for decreasing water balance intensities regarding food production and consumption.

Suggested Citation

  • Yohei Yamaguchi & Naoki Yoshikawa & Koji Amano & Seiji Hashimoto, 2021. "Decomposition Analysis of Global Water Supply-Demand Balances Focusing on Food Production and Consumption," Sustainability, MDPI, vol. 13(14), pages 1-32, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7586-:d:589870
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/14/7586/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/14/7586/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Zhang, Chenjun & Wu, Yusi & Yu, Yu, 2020. "Spatial decomposition analysis of water intensity in China," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
    3. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    4. Sun, J.W. & Malaska, P., 1998. "CO2 emission intensities in developed countries 1980–1994," Energy, Elsevier, vol. 23(2), pages 105-112.
    5. Ang, B. W. & Liu, F. L. & Chew, E. P., 2003. "Perfect decomposition techniques in energy and environmental analysis," Energy Policy, Elsevier, vol. 31(14), pages 1561-1566, November.
    6. Sun, J. W., 1998. "Changes in energy consumption and energy intensity: A complete decomposition model," Energy Economics, Elsevier, vol. 20(1), pages 85-100, February.
    7. Hoekstra, Rutger & van den Bergh, Jeroen C. J. M., 2003. "Comparing structural decomposition analysis and index," Energy Economics, Elsevier, vol. 25(1), pages 39-64, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    2. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    3. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2012. "Combining physical and economic output data to analyse energy and CO2 emissions trends in industry," Energy Policy, Elsevier, vol. 49(C), pages 422-429.
    4. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    5. Bor, Yunchang Jeffrey, 2008. "Consistent multi-level energy efficiency indicators and their policy implications," Energy Economics, Elsevier, vol. 30(5), pages 2401-2419, September.
    6. Carlino, Laurent & Coppens, François & González, Javier & Ortega, Manuel & Pérez-Duarte, Sébastien & Rubbrecht, Ilse & Vennix, Saskia, 2017. "Decomposition techniques for financial ratios of European non-financial listed groups," Statistics Paper Series 21, European Central Bank.
    7. Chen, Jiandong & Xu, Chong & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2019. "Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach," Energy Economics, Elsevier, vol. 78(C), pages 589-597.
    8. Lenzen, Manfred, 2006. "Decomposition analysis and the mean-rate-of-change index," Applied Energy, Elsevier, vol. 83(3), pages 185-198, March.
    9. Shigemi Kagawa & Yuriko Goto & Sangwon Suh & Keisuke Nansai & Yuki Kudoh, 2012. "Accounting for Changes in Automobile Gasoline Consumption in Japan: 2000–2007," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 1(1), pages 1-27, December.
    10. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    11. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    12. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    13. Banie Naser Outchiri, 2020. "Contributing to better energy and environmental analyses: how accurate are decomposition analysis results?," Cahiers de recherche 20-11, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    14. Huang, Yun-Hsun, 2020. "Examining impact factors of residential electricity consumption in Taiwan using index decomposition analysis based on end-use level data," Energy, Elsevier, vol. 213(C).
    15. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry," Energy, Elsevier, vol. 68(C), pages 688-697.
    16. Jeong, Kyonghwa & Kim, Suyi, 2013. "LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector," Energy Policy, Elsevier, vol. 62(C), pages 1245-1253.
    17. Edyta Sidorczuk-Pietraszko, 2020. "Spatial Differences in Carbon Intensity in Polish Households," Energies, MDPI, vol. 13(12), pages 1-21, June.
    18. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    19. Zhiqian Yu & Dalia Streimikiene & Tomas Balezentis & Rimantas Dapkus, 2017. "Final Energy Consumption Trends and Drivers in Czech Republic and Latvia," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 19(46), pages 866-866, August.
    20. Štreimikienė, Dalia & Balezentis, Tomas, 2016. "Kaya identity for analysis of the main drivers of GHG emissions and feasibility to implement EU “20–20–20” targets in the Baltic States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1108-1113.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:14:p:7586-:d:589870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.